接下来,使用filter()方法过滤掉包含要删除的列表的行。可以使用lambda表达式来定义过滤条件。 代码语言:txt 复制 filtered_rdd = rdd.filter(lambda row: row['column_name'] not in list_to_remove) 在上面的代码中,column_name是DataFrame中包含要删除的列表的列的名
# Filter NOT IS IN List values #These show all records with NY (NY is not part of the list) df.filter~df.state.isin(li)).show() df.filter(df.stateisin(li)==False).show() 12.
5. Filter Based on List Values Theisin()function from thePython Columnclass allows you to filter a DataFrame based on whether the values in a particular column match any of the values in a specified list. And, to check not isin() you have to use the not operator (~) # Filter IS IN...
用来检查用户是否对某个节点进行过投票。...def downvoted_by(self, user): return self.down_votes.filter(user=user).exists()然后,在视图中,我们可以使用这些方法来检查用户是否对某个帖子进行过投票...down="{%if node.pk in downvoted_comments %}{% endif %}" ...通过上述方法,可以高效...
pyspark的filter多个条件如何设置 pyspark dataframe collect,classpyspark.sql.DataFrame(jdf,sql_ctx)分布式的列式分组数据集(1.3版本新增)一个DataFrame对象相当于SparkSQL中的一个关系型数据表,可以通过SQLContext中的多个函数生成,如下例:people=sqlContext.read.parq
在这里,我们使用filter()函数过滤了行,并在filter()函数内部指定了text_file_value.contains包含单词"Spark",然后将这些结果放入了lines_with_spark变量中。 我们可以修改上述命令,简单地添加.count(),如下所示: text_file.filter(text_file.value.contains("Spark")).count() ...
for i in x: if i != 0: cnt +=1 return cnt df = df.withColumn("scene_seq", get_array_int(df.scene_seq)) df = df.withColumn('scene_num', get_nozero_num(df.scene_seq)) df = df.filter(df.scene_num > 61) df_seq = df.select("role_id","scene_seq") ...
rdd2=rdd1.filter(lambda x:x%2==1) print(rdd2.collect()) #停止SparkContext对象的运行(停止PySpark程序) sc.stop() 输出: 24/11/11 21:20:46 WARN Shell: Did not find winutils.exe: java.io.FileNotFoundException: java.io.FileNotFoundException: HADOOP_HOME and hadoop.home.dir are unset....
# 计算一列空值数目 df.filter(df['col_name'].isNull()).count() # 计算每列空值数目 for col in df.columns: print(col, "\t", "with null values: ", df.filter(df[col].isNull()).count()) 平均值填充缺失值 from pyspark.sql.functions import when import pyspark.sql.functions as F #...
filter((df.Status == 1))['Country','Platform','Status'] df_new.show() +---+---+---+ | Country|Platform|Status| +---+---+---+ | India| Yahoo| 1| |Indonesia| Bing| 1| | Malaysia| Google| 1| |Indonesia| Bing| 1| | Malaysia| Google| 1| |Indonesia| Yahoo| 1| |Indo...